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Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

Showing 21112120 of 9051 papers

TitleStatusHype
A Quality-based Syntactic Template Retriever for Syntactically-controlled Paraphrase GenerationCode0
How Inclusively do LMs Perceive Social and Moral Norms?Code0
How Far Can We Extract Diverse Perspectives from Large Language Models?Code0
Class Incremental Learning with Multi-Teacher DistillationCode0
How Does A Text Preprocessing Pipeline Affect Ontology Syntactic Matching?Code0
Towards control of opinion diversity by introducing zealots into a polarised social groupCode0
HiTR: Hierarchical Topic Model Re-estimation for Measuring Topical Diversity of DocumentsCode0
Higher-Order Message Passing for Glycan Representation LearningCode0
Hierarchical Reinforcement Learning via Advantage-Weighted Information MaximizationCode0
High-dimensional Assisted Generative Model for Color Image RestorationCode0
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